The biggest opportunity for AI? It might not be what you think…
For the last couple of years, most of the noise in AI has centered on Large Language Models (LLMs). But as impressive as they are, LLMs are not the endgame. In fact, they’re missing something crucial: the ability to sense, interact with, and shape the physical world.
Physical AI is a fast-emerging field that draws its knowledge from direct experiences, such as spatial context, tactile feedback, and movement. And while it includes robotics, it goes beyond this, also encompassing areas like shelf intelligence and environmental monitoring.
In physical AI, advanced sensory data and computer vision are used to adapt and respond to real-time, unpredictable interactions and events, much like humans do in our day-to-day lives. It represents a foundational change that will reduce costs, mitigate risks, and redefine physical industries such as retail, agriculture, construction, manufacturing, and logistics.
What is physical AI?
Physical AI is AI that learns from cameras, sensors, audio, and motion to act in real time in the physical world.
Physical AI systems are multi-modal, gathering intelligence from multiple real-world sensors and inputs. They use this holistic understanding to respond to complex problems in context and real time.
These systems are trained not just on language, but on physical experience. LLMs consume internet-scale text to respond to questions. In contrast, physical AI models learn from the real world.
It’s the convergence of machine learning, sensors, and edge computing into deployable intelligence for physical industries.
While robotics often grabs the spotlight, physical AI is just as powerful when used to guide, support, and augment human workers on the frontlines. Stores, farms, and warehouses need AI that sees, moves, responds, and collaborates with humans and the environment in real time to grow their businesses and their bottom line.
And it doesn’t necessarily require massive infrastructure investment. With advances in edge computing, physical AI can run on-device, using the high-performance sensors already built into commodity hardware like the smartphone in your pocket.
Why does physical AI matter?
Physical AI matters because physical industries make up 75% of the world’s GDP. If you work in one of these, you’ve likely already experienced the limitations of LLMs. A chatbot can’t reduce warehouse downtime. A generative image tool can create a picture of a fully stocked grocery shelf, but that doesn’t help if your real shelves are empty.
Enterprises have accelerated their digital transformation efforts over the last few years, but one key area is lagging, namely the capture of data in the physical world.
Your world doesn’t run on clean data. It runs on barcode scans, out-of-stock alerts, unpredictable human movement, and fragile workflows that span the physical and digital.
Physical AI fills this gap. It creates AI that:
Understands shelf layouts and stock conditions visually
Detects anomalies on production lines from audio or thermal cues
Enhances frontline productivity with real-time actionable insights
Learns from real-world context, not labeled data sets
This is AI for the messy edge of operations, where the real-time stakes are high and the tolerance for error is low.
What are real-world examples of physical AI?
Three real-world examples of physical AI are FANUC’s Zero Down Time (ZDT), Archetype AI, and Scandit Smart Data Capture.
FANUC's Zero Down Time (ZDT) platform uses sensor-fed machine learning to predict robot failures in automotive plants and prevent downtime before it even starts.
Archetype AI has developed Newton, a foundation model trained not on text, but on physical inputs like microphones and radar. It powers everything from factory safety monitoring to environmental sensing.
Scandit Smart Data Capture meanwhile, is deploying physical AI in areas such as shelf intelligence, combining barcode scanning, text recognition, object recognition, and augmented reality to help store associates identify and fix inventory issues.
These examples aren’t experiments. They’re early signals of a broader transformation.
And LLMs won’t save your picking operations. What will? An AI system that can see your warehouse, understand your workflows, and adapt in real time—and that doesn’t necessarily need a months-long infrastructure project to get up and running.
It can be retrofitted to existing crop sprayers rather than requiring expensive investment. Farmers using See & Spray not only reduce environmental impact by reducing herbicide use, but see average cost savings of 59%.
This is not about speculative AI futures. It’s about getting ahead of what’s next, while your competitors are still prompting ChatGPT to draft next year’s strategic plan.
How to prepare your roadmap for physical AI
1 Audit your physical interfaces
Where are people manually gathering data? Which physical processes bottleneck digital systems? Start by identifying gaps in your current stack where real-world intelligence is missing.
2 Run pilots with real-world feedback loops
Avoid “slideware” AI. Look for opportunities to trial solutions that work on the devices and environments you already operate in. Frontline usability and adaptability are key.
3 Prioritize adaptability over perfection
Unlike classic automation, physical AI evolves and improves with use. Build infrastructure that establishes processes for iterative training and feedback loops.
4 Choose vendors who think beyond APIs
Successful adoption won’t come from plug-and-play SDKs alone. Look for partners who understand your operational realities, can co-develop if needed, and bring a strong vision for the AI-powered edge.
Waiting is risky. Moving early on physical AI is strategic.
Physical AI isn’t just another tool in your stack. It’s a new paradigm that redefines how enterprise systems interact with reality.
And for product and IT leaders leading transformation in complex, physical environments, this is your category to shape.
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